System and method for image registration in medical imaging system

ABSTRACT

The present disclosure relates to a system and method for image registration and image subtraction. The technique includes perform acquiring data related to the image processing, performing a pre-processing of the images, performing an image registration, performing an image subtraction, performing a post-processing of the images and managing storage of the data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Application No.201510679427.3 filed on Oct. 14, 2015, the entire content of which ishereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to a system and method forimage processing, and more particularly, a system and method for thedetection of pathological changes from two or more medical imagesacquired at various times.

BACKGROUND

In medical image processing, the image registration may refer to theprocess of performing certain spatial transformation on an image, so asto facilitate comparisons of images of an object taken at differenttimes or stages of a disease as it progresses. The image registrationmay be widely used for the purposes of medical diagnosis and treatment.The image registration may aid doctors in visualizing and monitoringpathological changes in an identical object over time. For example, theimage registration may help doctors monitor the growth or shrinkage of alesion or nodule and may aid in detecting subtle changes in the densityof the lesion or nodule over time. Thus, it is desirable to improve theaccuracy of image registration in medical images.

SUMMARY

The present disclosure provided herein relates to medical imageprocessing. Specifically, the present disclosure relates to a system andmethod for generating temporal subtraction images. The generatedtemporal subtraction images may be obtained by performing a series ofimage registration on the images of an object taken at various times.

The images of an object taken at various stages may help doctors orradiologists to identify abnormalities and/or to determine changes whichhave occurred between two examinations. For example, the size and/or thelocation of a lesion at various stages of the object, as illustrated inthe images, may be compared to indicate the development of the lesion.However, it may be difficult to compare images taken at various times.For instance, if the images were taken during the various breathingstages of the object, breathing may impact the images.

A method of image registration may be performed on the images so as tofacilitate the comparison of different images taken at various times. Toperform an image registration, at least two images may be provided. Oneimage may be referred to the reference image. Another one may bereferred to the floating image. A spatial transformation may beperformed to deform the floating image to obtain an auxiliary image, andthe difference between the auxiliary image and the reference image maybe measured by a function, referred to as a cost function or anobjective function. For a chosen cost function, an image registrationmethod may utilize an optimization algorithm to reduce or substantiallyminimize the cost function based on the auxiliary image and thereference image, thus achieving an improved or optimal alignment betweenthe reference image and the auxiliary image. A subtraction image may beobtained based on subtracting the reference image from the auxiliaryimage from the improvement or optimization. Using the subtraction image,the differences between the reference image and the floating image maybe shown. The change at a region of interest may be demonstrated usingthe reference image, the floating image, and/or the subtraction image.If the floating image and the reference image are taken at differenttimes, the subtraction image may indicate a change at a region ofinterest over time, and may be referred to as a temporal subtractionimage.

Various ways of image registration may be designed by choosing a costfunction, a spatial transformation to be performed on the image data,and/or an optimization algorithm.

In an aspect of the present disclosure, a method for image registrationis provided. The method may include one or more of the followingoperations. A first image of an object may be designated as a referenceimage. The reference image may include at least a reference featurepoint and a reference structure. A second image of the object may beobtained. The second image may include a feature point and a structure.The feature point may correspond to the reference feature point of thereference image. The structure may correspond to the reference structureof the reference image. A first registration of the second image may beperformed to obtain a first registered image. The first registration mayinclude an affine transformation. The first registered image may includethe feature point and the structure. A second registration of the firstregistered image may be performed to obtain a second registered image.The second registration may include aligning the structure in the firstregistered image with the reference structure in the reference image.The second registered image may include the feature point. A thirdregistration of the second registered image may be performed to obtain athird registered image. The third registration may include aligning thefeature point in the second registered image with the reference featurepoint in the reference image. The third registered image may besubtracted from the reference image to obtain a subtraction image. Thesubtraction image may include the feature point or the structure.

In another aspect of the present disclosure, a non-transitory computerreadable storage medium including instructions is provided. Theinstructions, when executed by a processor, may cause the processor toeffectuate a method including one or more of the following operations. Afirst image of an object may be designated as a reference image. Thereference image may include at least a reference feature point and areference structure. A second image of the object may be obtained. Thesecond image may include a feature point and a structure. The featurepoint may correspond to the reference feature point of the referenceimage. The structure may correspond to the reference structure of thereference image. A first registration of the second image may beperformed to obtain a first registered image. The first registration mayinclude an affine transformation. The first registered image may includethe feature point and the structure. A second registration of the firstregistered image may be performed to obtain a second registered image.The second registration may include aligning the structure in the firstregistered image with the reference structure in the reference image.The second registered image may include the feature point. A thirdregistration of the second registered image may be performed to obtain athird registered image. The third registration may include aligning thefeature point in the second registered image with the reference featurepoint in the reference image. The third registered image may besubtracted from the reference image to obtain a subtraction image. Thesubtraction image may include the feature point or the structure.

In some embodiments, the first registration may be based on theoptimization of either mutual information or the mean squared error. Insome embodiments, the optimization method of the first registration maybe based on the downhill simplex method.

In some embodiments, the second registration may be based on the freeform deformation model transformation. In some embodiments, the secondregistration may be based on the optimization of either mutualinformation or the mean squared error. In some embodiments, the secondregistration may be based on the L-BFGS method.

In some embodiments, the third registration may be based on the Demonsmodel transformation. In some embodiments, the third registration may bebased on the optimization of either mutual information or the meansquared error. In some embodiments, the third registration may be basedon the L-BFGS method.

In some embodiments, the first image and the second image may be takenat different times.

In some embodiments, the first image, the second image, and thesubtraction image may be displayed on a same display device. One or moreof them may be displayed in a row or in a column.

In some embodiments, the reference feature point in the reference imageand the feature point in the second image may be displayed in thesubtraction image.

In some embodiments, the subtraction image may be fused with thereference image for displaying a change in the reference feature pointand/or the reference structure between the reference image and thesecond image. Merely by way of example, a doctor may determine whetherthe reference feature point (e.g., a lesion) has changed by observingthe difference between the reference feature point and the featurepoint. Such information may provide guidance for the doctor in diagnosisand/or to improve or revise a treatment plan.

In some embodiments, besides including a series of image registration,the method may further include identifying a structure in the secondimage within a region of interest in the subtraction image, andquantifying the pathological changes in the region of interest.

In a further aspect of the present disclosure, a system of imageprocessing is provided. The system of image processing may include anacquisition module and an image processing module. The acquisitionmodule may acquire a first image and a second image. The imageprocessing module may designate the first image as a reference image.The image processing module may designate the second image as a floatingimage. The image processing module may perform image registration basedon the reference image and the floating image, and perform imagesubtraction based on the image registration and the reference image toacquire a temporal subtraction image.

In some embodiments, the image processing module may include apost-processing unit to perform lesion detection and lesion measurement.In some embodiments, the image processing module may include a controlunit to control the performance of a series of registrations.

In some embodiments, the imaging system may include a ComputedTomography (CT) system, a Digital Radiography (DR) system, a ComputedTomography-Positron Emission Tomography (CT-PET) system, a ComputedTomography-Magnetic Resonance Imaging (CT-MRI) system, an X-ray securitysystem or an X-ray foreign matter detection system, or the like, or anycombination thereof.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 shows an exemplary diagram of an image processing systemaccording to some embodiments of the present disclosure;

FIG. 2 illustrates an exemplary flowchart of image processing accordingto some embodiments of the present disclosure;

FIG. 3 shows an exemplary diagram of a processing module according tosome embodiments of the present disclosure;

FIG. 4 is an exemplary flowchart illustrating a processing of imageregistration according to some embodiments of the present disclosure;

FIG. 5 is an exemplary flowchart illustrating a process for imageprocessing according to some embodiments of the present disclosure;

FIG. 6 is an exemplary diagram of an image registration unit accordingto some embodiments of the present disclosure;

FIG. 7 is an exemplary flowchart illustrating a process for performing aregistration according to some embodiments of the present disclosure;

FIG. 8A through FIG. 8C demonstrate some exemplary images obtained byperforming the registration as illustrated in FIG. 7;

FIG. 9 is an exemplary flowchart illustrating a process for performing aregistration according to some embodiments of the present disclosure;

FIG. 10A through FIG. 10D demonstrate some exemplary images obtained byperforming the registration as illustrated in FIG. 9;

FIG. 11 is an exemplary flowchart illustrating a process for performinga registration according to some embodiments of the present disclosure;

FIG. 12A through FIG. 12D demonstrate some exemplary images obtained byperforming the registration as illustrated in FIG. 11;

FIG. 13 is an exemplary flowchart illustrating a process for performinga series of registrations according to some embodiments of the presentdisclosure;

FIGS. 14A-14F illustrate six CT images that were generated based onimage registration according to some embodiments of the presentdisclosure; and

FIGS. 15A-15F illustrate six X-ray images that were generated based onimage registration according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof. It willbe further understood that the terms “constructed” and “reconstruct”,when used in this disclosure, may represent a similar process that animage may be transformed from data.

In some embodiments, the medical imaging system may be operated undervarious modalities, including but not limited to, Digital SubtractionAngiography (DSA), Magnetic Resonance Imaging (MRI), Magnetic ResonanceAngiography (MRA), Computed tomography (CT), Digital Radiography (DR),Computed Tomography Angiography (CTA), Ultrasound Scanning (US),Positron Emission Tomography (PET), Single-Photon Emission ComputerizedTomography (SPECT), CT-MR, CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US,SPECT-US, TMS (transcranial magnetic stimulation)-MR, US-CT, US-MR,X-ray-CT, X-ray-MR, X-ray-portal, X-ray-US, Video-CT, Vide-US, or thelike, or any combination thereof. This is understood that the followingdescriptions are provided in connection with medical image processingfor illustration purposes and not intended to limit the scope of thepresent disclosure. The image processing disclosed herein may be usedfor purposes other than medical treatment or diagnosis. For instance,the image processing may be used for purposes of detecting a fracturewithin a structure or its progression over time, a non-uniform portionwithin a piece of material, etc.

The radiation used by a medical imaging system may include a particleray, a photon ray, or the like, or any combination thereof. The particleray may include neutron, proton, electron, μ-meson, heavy ion, or thelike, or any combination thereof. The photon beam may include X-ray,γ-ray, α-ray, β-ray, ultraviolet, laser, or the like, or any combinationthereof. In some embodiments, the image registration may be theregistration of a CT image. For example, various CT images obtained byscanning the lung area of a patient may be processed, or sometimeswarped, to check the status of the lung area of the patient. In someembodiments, the image registration may be the registration of a DRimage. For example, various DR images exhibiting the cerebral area of apatient over time may be warped and fused together for furtherprocessing.

In some embodiments, the object may be a human being, an animal, anorgan, a texture, a region, a lesion, a tumor, or the like, or anycombination thereof. Merely by way for example, the object may include ahead, a breast, a lung, a trachea, a pleura, a mediastinum, an abdomen,a long intestine, a small intestine, a bladder, a gallbladder, a triplewarmer, a pelvic cavity, a backbone, extremities, a skeleton, a bloodvessel, or the like, or any combination thereof. In some embodiments,the medical image may include a 2D image and/or a 3D image. In someembodiments, the 3D image may include a series of 2D slices or 2Dlayers.

For illustration purposes, the following description is provided to helpbetter understanding an image processing. It is understood that this isnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, a certain amount of variations,changes and/or modifications may be deducted under guidance of thepresent disclosure. However, those variations, changes and/ormodifications do not depart from the scope of the present disclosure.

The present disclosure provided herein relates to medical imageprocessing. Specifically, the present disclosure relates to a system andmethod for image registration. Image registration may be used in remotesensing, multispectral classification, environmental monitoring, imagemosaicking, weather forecasting, creating super-resolution images. Imageregistration may also be widely sued in diagnosis and treatment ofpatient in medicine such as combining computer tomography (CT) andnuclear magnetic resonance (NMR) data to obtain more completeinformation about, for example, a patient, an object, weather, or thelike, or a change thereof. Image registration may also be used incartography (map updating), and in computer vision (target localization,automatic quality control). The process of image registration asillustrated in the present disclosure may be completely automated, toperform an automatic generation of the temporal subtraction imagesand/or fusion images. It may be embedded into a computer-aided andautomated medical diagnosis and treatment system.

FIG. 1 shows an exemplary diagram of an image processing systemaccording to some embodiments of the present disclosure. As described inFIG. 1, the image processing system may include an acquisition module110, a processing module 120, a storage module 130, an output module140, a network 150, and a server 160. The connection between differentunits may be wired or wireless. The wired connection may include using ametal cable, an optical cable, a hybrid cable, an interface, or thelike, or any combination thereof. The wireless connection may includeusing a Local Area Network (LAN), a Wide Area Network (WAN), aBluetooth, a ZigBee, a Near Field Communication (NFC), a Wi-Fi, aWireless a Wide Area Network (WWAN), or the like, or any combinationthereof.

The acquisition module 110 may acquire and/or send information relatedto image processing. The information may be acquired from the processingmodule 120, the storage module 130, the output module 140, the network150 the server 160, or the like, or any combination thereof. Theinformation may include data, such as a number, a text, an image, avoice, a force, a model, an algorithm, a software, a program, or thelike, or any combination thereof. For example, the information mayinclude information relating to an object, an operator, an instrument,an instruction, or the like, or any combination thereof. As used herein,the object may refer to a human being, an animal, an organ, a texture, aregion, a lesion, a tumor, or the like, or any combination thereof. Insome embodiments, the object may include a substance, a tissue, aspecimen, a body, or the like, or any combination thereof. The objectmay include a head, a breast, a lung, a pleura, a mediastinum, anabdomen, a long intestine, a small intestine, a bladder, a gallbladder,a triple warmer, a pelvic cavity, a backbone, an extremity, a skeleton,a blood vessel, or the like, or any combination thereof. Exemplaryinformation relating to an object may include ethnicity, citizenship,religion, gender, age, matrimony, height, weight, a medical history,job, personal habits, an organ or tissue to be examined, or the like, orany combination thereof. Exemplary information relating to an operatormay include a department, a title, prior experience, credentials, anoperating history, or the like, or any combination thereof, of theoperator. Exemplary information relating to an instrument may include anoperation status, the serial number of the medical imaging system, dateof operation or the like, or any combination thereof, of the imageprocessing system. Exemplary information relating to an instruction mayinclude a control command, an operation command, such as a command forselecting an image, or the like, or any combination thereof, of theimage processing system. Merely by way of example, the commands forselecting images may be an instruction to select one or more images inorder to assess a change in images.

The processing module 120 may process different kinds of informationreceived from different modules or units including the acquisitionmodule 110, the storage module 130, the output module 140, the network150, the server 160, or other modules or units that may generateinformation. The processing module 120 may process the data from theacquisition module 110 to generate a CT image of an object underexamination.

The processing module 120 may perform pre-processing, imageregistration, image subtraction and post-processing, or the like, or anycombination thereof. In some embodiments, the pre-processing may includeimage normalization, image segmentation, image reconstruction, imagesmoothing, suppressing, weakening and/or removing a detail, a mutation,a noise, or the like, or any combination thereof. In some embodiments,the image registration may include a series of registrations. In someembodiments, the post-processing may include a disease detection, adisease measurement, an image display, image storage management, other2D and/or 3D post-processing technique, or the like, or any combinationthereof. Merely by way of example, the images acquired after the imagesubtraction may contain noise, which may be treated in thepost-processing.

The processing module 120 may transfer the information from the storagemodule 130 to a particular form that may be identified, understood, orexecuted by the processing module 120, and it may process theinformation from the acquisition module 110 to retrieve data from thestorage module 130. The information from the acquisition module 110 tothe output module 140 may be processed by the storage module 130 firstlyso that it may be identified, understood, or executed by the processingmodule 120. The above description of the processing module 120 is merelyfor exemplary purposes, should not be understood as the onlyembodiments, and these examples do not limit the scope of the presentdisclosure.

In some embodiments, the processing module 120 may be a CentralProcessing Unit (CPU), an Application-Specific Integrated Circuit(ASIC), an Application-Specific Instruction-Set Processor (ASIP), aGraphics Processing Unit (GPU), a Physics Processing Unit (PPU), aDigital Signal Processor (DSP), a Field Programmable Gate Array (FPGA),a Programmable Logic Device (PLD), a Controller, a Microcontroller unit,a Processor, a Microprocessor, an ARM, or the like, or any combinationthereof.

The storage module 130 may be store information related to imageprocessing. In some embodiments, the storage module 130 may perform somestorage-related function, such as data consolidation and/or datapre-processing. The storage module 130 may acquire information from oroutput to other modules. Merely by way of example, the storage module130 may receive the data from the acquisition module, and then convey itto the processing module after possible pre-procession. The informationstored in storage module 130 may be acquired from or output to externalresource, such as a floppy disk, a hard disk, a CD-ROM, a networkserver, a cloud server, a wireless terminal, or the like, or anycombination thereof.

The storage module 130 may store information by the way of electric,magnetic, optical energy, or virtual storage resources, etc. The storagemodule that store information by the way of electric energy may includeRandom Access Memory (RAM), Read Only Memory (ROM), flash memory, or thelike, or any combination thereof. The storage module that storesinformation by the way of magnetic energy may include a hard disk, afloppy disk, a magnetic tape, a magnetic core memory, a bubble memory, aUSB flash drive, or the like, or any combination thereof. The storagemodule that store information by the way of optical energy may includeCD (Compact Disk), VCD (Video Compact Disk), or the like, or anycombination thereof. The storage module that store information by theway of virtual storage resources may include cloud storage, a virtualprivate network, and/or other virtual storage resources. The method tostore information may include sequential storage, link storage, hashstorage, index storage, or the like, or any combination thereof.

The output module 140 may output the information and/or data related toimage processing. For example, the output module 140 may display theimages acquired from the acquisition module 110 and/or the storagemodule 130, the output module 140 may display and/or output an imageprocessed by the processing module 120, The output module 140 mayinclude or be communicated with a personal computer, a desktop computer,a personal digital assistant, a somatosensory device, a mobile phone, ascreen, a monitor, a printer, or the like, or any combination thereof.The output module 140 may be connected with one or more externaldevices. The external devices may include a mouse, a keyboard, aremote-control unit, a sensor, or the like, or any combination thereof.

The network 150 may establish connection between any two of theacquisition module 110, the processing module 120, the storage module130, the output module 140, and the server 160 to communicate with eachother. The network 150 may be a single network or a combination ofdifferent networks. For example, the network 150 may be a local areanetwork (LAN), a wide area network (WAN), a public network, a privatenetwork, a proprietary network, a Public Telephone Switched Network(PSTN), the Internet, a wireless network, a virtual network, or thelike, or any combination thereof.

The server 160 may store and/or implement some information related toimage processing and some image processing algorithms. The server 160may be a cloud server. Merely by way of example, the server 160 may beimplemented in a cloud server that may provide storage capacity,computation capacity, or the like, or a combination thereof.

It should be noted that the above descriptions about the imageprocessing system is merely an example, should not be understood as theonly embodiment. Obviously, to those skilled in the art, afterunderstanding the basic principles of the connection between differentmodules, the modules and connection between the modules may be modifiedor varied without departing from the principles. The modifications andvariations are still within the scope of the current disclosuredescribed above. In some embodiments, these modules may be independent,and in some embodiments, part of the modules may be integrated into onemodule to work together. Merely by way of example, some information maybe stored in the server 160, some steps of image processing may beperformed by the server 160, functions of the acquisition module 110 andthe output module 130 may be performed in one module, the informationreceived by the acquisition module 110 may be from the server 160.

FIG. 2 illustrates an exemplary flowchart of image processing accordingto some embodiments of the present disclosure. In step 201, at least twoimages of an object may be acquired. The images may be obtained via theacquisition module 110. The images may be generated at different times.For example, at least one image may be generated at the early stage oflung cancer of an object, and at least one image may be generated at alate stage of lung cancer of the same object. The two images may be amono-modality image. The images may be acquired by the samemono-modality imaging device or the same multimodality imaging devices.The images may be acquired by different mono-modality imaging device.For instance, the images may be generated by Digital SubtractionAngiography (DSA), Magnetic Resonance Imaging (MRI), Magnetic ResonanceAngiography (MRA), Computed tomography (CT), Digital Radiography (DR),Computed Tomography Angiography (CTA), Ultrasound Scanning (US),Positron Emission Tomography (PET), Single-Photon Emission ComputerizedTomography (SPECT), CT-MR, CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US,SPECT-US, TMS (transcranial magnetic stimulation)-MR, US-CT, US-MR,X-ray-CT, X-ray-MR, X-ray-portal, X-ray-US, Video-CT, Vide-US, or thelike, or any combination thereof. In some embodiments, the images may betwo CT images of an object generated at different times and/or atdifferent hospitals. Merely by way of example, the images may includetwo DR images of an object taken at different times and/or differenthospitals using different imaging devices. The imaging devices may be ofthe same type or different types. One image may be set as a referenceimage. As used herein, a reference image may refer to an image taken atan early time point. For example, the reference image may be an image ofthe object at an initial stage of lung cancer at an early time, showingthe status of the object and the distribution of the lesion or nodulewithin a lung area. As used herein, a floating image, or a dynamicimage, may refer to an image of a same or similar area as the referenceimage. A floating image may be taken at a different time than thereference image. For instance, a floating image of the object may showthe status of the same or similar lung area of the object at a latertime, such as the distribution or density of the lesion or nodule withinthe lung area.

In step 202, the two images of the object may be subject topre-processing. The pre-processing procedure may be performed by theacquisition module 110. Alternatively, the pre-processing procedure maybe performed by the processing module 120. In some embodiments, thepre-processing may include a way of identification of rib area in amedical image. For example, a medical image exhibiting the lung area maybe obtained first. For example, a method in the Chinese Application No.201310170102.3 was disclosed to segment the lung area out of the image.A pre-processing may be performed on the medical image to locate therib(s) within the lung area in the image. The pre-processing may beperformed based on a Hough template. The Hough template may be utilizedto perform the Hough transformation on the lower boundary part of theribs within the lung area. Based on the value of the gravity center ofthe Hough template, a substantially optimal Hough template may bedetermined. The lower boundary part of the ribs corresponding to thesubstantially optimal Hough template may be distilled, and made smoothto be used as a baseline template for the image. Using the baselinetemplate, a generalized Hough transformation may be performed at thelower boundary part of the rib(s) within the lung area to furnish aninitial positioning. A bi-lateral dynamic programming algorithm may thenbe performed to segment the upper and lower boundary of the ribs, andtransfer the result of segmentation backward to the image, so as toseparate the rib(s) within the lung area. In some embodiments, thepre-processing may include image normalization, image segmentation,image recognition, image reconstruction, image smoothing, suppressing,weakening and/or removing a detail, a noise, or the like, or anycombination thereof.

Image segmentation may refer to the process of partitioning a digitalimage into multiple segments. In some embodiments, the method of asegmentation may include a threshold segmentation, a region growingsegmentation, a region split and/or merge segmentation, an edge tracingsegmentation, a statistical pattern recognition, a C-means clusteringsegmentation, a deformable model segmentation, a graph searchsegmentation, a neural network segmentation, a geodesic minimal pathsegmentation, a target tracking segmentation, an atlas-basedsegmentation, a rule-based segmentation, a coupled surface segmentation,a model-based segmentation, a deformable organism segmentation, or thelike, or any combination thereof. In some embodiments, the segmentationmethod may be performed in a manual mode, a semi-automatic mode, or anautomatic mode. The three modes may allow a user or an operator tocontrol the image processing in various degrees. In some embodiments ofthe manual mode, a parameter of the segmentation may be determined bythe user or the operator. Exemplary parameters may include a thresholdlevel, a homogeneity criterion, a function, an equation, an algorithm, amodel, or the like, or any combination thereof. In some embodiments ofthe automatic mode, the segmentation may be incorporated with someinformation about a desired object including, e.g., a prioriinformation, an optimized method, an expert-defined rule, a model, orthe like, or any combination thereof. The information may also beupdated by training or self-learning. In some embodiments of thesemi-automatic mode, the user or the operator may supervise thesegmentation process to a certain extent.

Image smoothing may refer to a process of removing noise digitally andimprove the quality of an image. The processing of image smoothing maybe in a spatial domain and/or a frequency domain. In some embodiments,smoothing in the spatial domain may include processing image pixelsand/or voxels directly. Smoothing in the frequency domain may includeprocessing a transformation value firstly acquired from the image andthen inversely transform the transformation value into a spatial domain.Exemplary image smoothing may include a median smoothing, a Gaussiansmoothing, a mean smoothing, a normalized smoothing, a bilateralsmoothing, or the like, or any combination thereof.

In step 203, a series of image registration may be performed on thefloating image to obtain one or more desired target images forcomparison with the reference image. In some embodiment, a series ofcoarse registration, fine registration, and super-fine registration maybe performed sequentially on the floating image, or an image resultedtherefrom (e.g., an auxiliary image described elsewhere in the presentdisclosure. An image registration may include choice of a group ofspatial transformations, a designation of a cost function, and anoptimization method. The group of spatial transformations may describespecific spatial transformations that may be performed on the floatingimages. In some embodiment, the group of spatial transformations may bea group of translations. In some embodiments, the group of spatialtransformations may be a group of rigid motions. In some embodiments,the group of spatial transformations may be a group of affinetransformations. The group of spatial transformation may also be basedon an elastic model or a fluid model.

A cost function may be used to measure the difference between twoimages. In some embodiments, the cost function may be mutual information(MI) or relative entropy between the two images. Based on informationtheory, mutual information may represent the amount of information thatone image may contain about a second image. Mutual information may bemaximized by aligning the two images in an optimal way. For illustrationpurposes, mutual information between a first image A and a second imageB may be expressed as Equation (1) below:

C _(similarity)(A,B)=H(A)+H(B)−H(A,B),  (1)

wherein H(A) and H(B) may denote the marginal entropies of A, B, andH(A,B) may denote their joint entropy calculated from the jointhistogram of A and B.

In some embodiments, the cost function may be normalized mutualinformation (NMI) between the two images. The normalized mutualinformation may be computed using the image entropies according toEquation (2):

$\begin{matrix}{{{C_{similarity}\left( {A,B} \right)} = \frac{{H(A)} + {H(B)}}{H\left( {A,B} \right)}},} & (2)\end{matrix}$

wherein H(A) and H(B) may denote the marginal entropies of A, B, andH(A,B) may denote their joint entropy calculated from the jointhistogram of A and B.

In some embodiments, the cost function may be given as a mean squarederror (MSE) between the two images. In some embodiments, the crosscorrelation between the two images may be designated as the costfunction. In some embodiments, the cost function may be given as a sumof squared intensity differences between the two images.

It should be noted that the above description of the cost function isprovided for the purposes of illustration, not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, various variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications may not depart the protecting scope of the presentdisclosure. For example, the cost function may be Hybrid-NMI.

In step 203, an optimization method may be selected based on the natureof the cost function and the group of spatial transformations selected.In some embodiments, the optimization of a cost function may involve aplurality of parameters. For example, for a free-form deformation (FFD)algorithm based on the B-spline, with the control nodes of 10×10×10, theparameters for describing the degree of freedom may reach up to13×13×13=2197. The complexity of optimization may depend on the size ofmesh, i.e. the distance between the control nodes, because a small meshsize may involve a great amount of computation. The optimization methodmay include a Powell method, a downhill simplex method, a gradientdescent method, a downhill simplex method, a deepest gradient descendingmethod, a conjugate gradient method, a pseudo-Newton method, aquasi-Newton method, a least-squares and Gauss-Newton method, aBroyden-Fletcher-Goldfarb-Shanno (BFGS) method, a limited-memoryBroyden-Fletcher-Goldfarb-Shanno (L-BFGS) method, a simulated annealingmethod, an ant colony optimization (ACO) method, a genetics algorithm(GA), a Levenberg-Marquardt optimization method, a geometric hashingmethod, a particle swarm optimization (PSO) method, a firefly algorithm(FA) method, or the like, or a combination thereof.

In step 204, the post-processing may be performed. In some embodiments,a procedure of post-processing may be performed on the result imagesfrom step 203. A subtraction image may be generated by subtracting afloating image, or an image relating to the floating image (e.g., anauxiliary image described elsewhere in the present disclosure), from areference image. In some embodiments, the floating image may include asame or similar region as the reference image. In some embodiments, thefloating image may be taken at a different time. The subtraction imageobtained by way of subtracting a floating image taken at a differenttime than the reference image may be referred to as a temporalsubtraction image. The temporal subtraction image may show thedifferences between the transformed image and the reference image. Insome embodiments, the subtraction image (e.g., a temporal subtractionimage) may be combined with the reference image to provide a fusedimage. The fused image, and/or the reference image, as well as otherrelevant images (e.g., one or more floating images), may be furtherprocessed to designate a region of interest. The size of the region ofinterest may be calculated, and the location of the region of interestmay be labelled for future use. In some embodiments, the temporalsubtraction image and/or the fused image may be provided to a displaydevice for display. The post-processing may include a disease detection,a disease measurement, an image display, image storage management, other2D and/or 3D post-processing technique, or the like, or any combinationthereof. Merely by way of example, the images acquired after the imagesubtraction may contain noise, which may be treated in thepost-processing.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conduct under the teachingof the present disclosure. However, those variations and modificationsmay not depart from the protecting of the present disclosure. Forexample, step 201, step 202, step 203 and step 204 may be performedsequentially at an order other than that described above in FIG. 2. Atleast two steps of step 201, step 202, step 203 and step 204 may beperformed concurrently. Step 201, step 202, step 203 and step 204 may bemerged into a single step or divided into a number of steps. Inaddition, one or more other operations may be performed before/after orin performing step 201, step 202, step 203, and step 204. At least oneof step 201, step 202, step 203 and step 204 may be unnecessary and maybe omitted.

FIG. 3 shows an exemplary diagram of a processing module according tosome embodiments of the present disclosure. As described in FIG. 3, theprocessing module may include a pre-processing unit 310, a control unit320, a registration unit 330, a storage unit 340, and a post-processingunit 350. The communication or data exchange between different units maybe via a wired connection or a wireless connection. The wired connectionmay include using a metal cable, an optical cable, a hybrid cable, aninterface, or the like, or any combination thereof. The wirelessconnection may include using a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),a Wi-Fi, a Wireless a Wide Area Network (WWAN), or the like, or anycombination thereof.

The pre-processing unit 310 may acquire and/or send information relatingto image processing. The information may be acquired from the controlunit 320, the registration unit 330, the storage unit 340, or thepost-processing unit 350, or the like, or any combination thereof. Theinformation may include data, such as a number, a text, an image, avoice, a force, a model, an algorithm, a software, a program, or thelike, or any combination thereof. For example, the information mayinclude information relating to an object, an operator, an instrument,an instruction, or the like, or any combination thereof, as describedelsewhere in the present disclosure.

The control unit 320 may coordinate the execution of image registration.The control unit 320 may obtain and/or forward information relating toan object, an operator, an instrument, and an instruction, etc. from/tothe pre-processing unit 310. The control unit 320 may forward at leastpart of the obtained information to the registration unit 330. Forexample, various images of an object at different times may be forwardedto the registration unit 330 for processing. The control unit 320 maydetermine the type of registration to be performed on the images. Forexample, the control unit 320 may determine the group of spatialtransformations, the choice of cost function, and the optimizationmethod. In some embodiments, the control unit 320 may determine a typeof coarse registration (see below for detailed description of coarseregistration) to be performed. Merely by way of example, the coarseregistration may include a group of affine transformations, and the costfunction of the normalized mutual information between the images, andthe optimization method of the downhill simplex method.

The registration unit 330 may perform the specific image registration onthe obtained images. The type of image registration may be selected bythe control unit 320. Alternatively, the type of image registration maybe selected by the registration unit 330 itself. Based on the selectedimage registration, the registration unit 330 may choose thecorresponding spatial transformation by setting the appropriate meshsize for the images, and choosing the parameters involved in the spatialtransformation. The registration unit 330 may determine the type ofsolver for the selected cost function and the optimization method. Thesolver may be a matrix solver. In some embodiments, the solver may be anordinary differential equation (ODE) solver. In some embodiments, thesolver may be a partial differential equation (PDE) solver. Forinstance, the PDE solver may be selected when an elastic model or afluid model for modelling the offsets of non-rigid meshes is involved.

The storage unit 340 may store information relating to image processing.In some embodiments, the storage unit 340 may store the algorithmsrelated to image registration. For example, algorithms may includealgorithms relating to a coarse registration, algorithms related to afine registration, algorithms related to a super-fine registration.

The storage unit 340 may acquire information from or provide informationto other modules. Merely by way of example, some information may beacquired from or output to external resource, such as a floppy disk, ahard disk, a CD-ROM, a wireless terminal, or the like, or anycombination thereof.

The storage unit 340 may store information by the way of electric,magnetic, optical energy, or virtual storage resources, etc. The storagemodule that store information by the way of electric energy may includeRandom Access Memory (RAM), Read Only Memory (ROM), or the like, or anycombination thereof. The storage module that stores information by theway of magnetic energy may include a hard disk, a floppy disk, amagnetic tape, a magnetic core memory, a bubble memory, a USB flashdrive, or the like, or any combination thereof. The storage module thatstore information by the way of optical energy may include CD (CompactDisk), VCD (Video Compact Disk), or the like, or any combinationthereof. The storage module that store information by the way of virtualstorage resources may include cloud storage, a virtual private network,and/or other virtual storage resources. The method to store informationmay include sequential storage, link storage, hash storage, indexstorage, or the like, or any combination thereof.

The post-processing unit 350 may perform post-processing. Thepost-processing may include disease detection, disease measurement,image display, image storage management, or other 2D and/or 3Dpost-processing technique or the like, or any combination thereof.Merely by way of example, the images acquired after the imagesubtraction may contain noise, which may be treated in thepost-processing. The display of images may be provided by thepost-processing unit 350.

It should be noted that the above description of the image processingmodule is provided for the purposes of illustration, not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the protecting scope of the presentdisclosure. For example, the data acquired by the pre-processing unit310 may be sent to the storage unit 340 and/or the registration unit330. The control unit 320 may be combined with the storage unit 340 towork as a single operative unit. A subtraction unit may

FIG. 4 is an exemplary flowchart illustrating a process for imageregistration according to some embodiments of the present disclosure. Instep 401, a model for image registration may be determined. The modelfor image registration may designate the group of spatialtransformations. For example, the three-dimensional affinetransformation may take the following form:

$\begin{matrix}{{\begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime}\end{bmatrix} = {{\begin{bmatrix}a_{11} & a_{12} & a_{13} \\a_{21} & a_{22} & a_{23} \\a_{31} & a_{32} & a_{33}\end{bmatrix}\begin{bmatrix}x \\y \\z\end{bmatrix}} + \begin{bmatrix}t_{x} \\t_{y} \\t_{z}\end{bmatrix}}},} & (3)\end{matrix}$

wherein (x′, y′, z′) may be the coordinate of transformed voxels, (x, y,z) may be the coordinate of the voxel before the transformation. Thecoefficients a₁₁, a₁₂, a₁₃, a₂₁, a₂₂, a₂₃, a₃₁, a₃₂, a₃₃, t_(x), t_(y),t_(z) may be the parameters to be optimized. In another example, thespatial transformation may be based on the B-spline transformationmodel, where the B-spline function is described by:

$\begin{matrix}\begin{matrix}{\begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime}\end{bmatrix} = {T\left( \begin{bmatrix}x \\y \\z\end{bmatrix} \right)}} \\{{= {\begin{bmatrix}x \\y \\z\end{bmatrix} + \begin{bmatrix}{\sum\limits_{l = 0}^{3}{\sum\limits_{m = 0}^{3}{\sum\limits_{n = 0}^{3}{{B_{l}(u)}{B_{m}(v)}{B_{n}(w)}{dx}_{{i + l},{j + m},{k + n}}}}}} \\{\sum\limits_{l = 0}^{3}{\sum\limits_{m = 0}^{3}{\sum\limits_{n = 0}^{3}{{B_{l}(u)}{B_{m}(v)}{B_{n}(w)}{dy}_{{i + l},{j + m},{k + n}}}}}} \\{\sum\limits_{l = 0}^{3}{\sum\limits_{m = 0}^{3}{\sum\limits_{n = 0}^{3}{{B_{l}(u)}{B_{m}(v)}{B_{n}(w)}{dz}_{{i + l},{j + m},{k + n}}}}}}\end{bmatrix}}},}\end{matrix} & (4)\end{matrix}$

wherein

u=x/n _(x) −└x/n _(x)┘,  (5)

v=y/n _(y) −└y/n _(y)┘,  (6)

w=z/n _(z) −└z/n _(z)┘,  (7)

B ₀(u)=(1−u)³/6,  (8)

B ₁(u)=(3u ³−6u ²+4)/6,  (9)

B ₂(u)=(−3u ³+3u ²+3u+1)/6,  (10)

B ₃(u)=u ³/6,  (11)

and

i=└x/n _(x)┘−1,j=└n _(y)┘−1,k=└z/n _(z)┘−1,  (12)

may be the indices of a control point, and dx, dy, dz may give theoffsets along the X direction, the Y direction, and the Z direction,respectively, of the control points. The set of control points gives theframework of the B-spline transformation, so the movement of the controlpoints along the X direction, the Y direction, and the Z direction, givethe parameters to be optimized during the registration.

In some other embodiments, the spatial transformation may be based onmodelling of a local deformation field. For example, if the imageregistration process may be based on image forces and Gaussianregulation of the deformation field, such as a demons method. Thetransformation model of the demons method may be a local displacementfield, i.e., at each and every pixel x, the deformation may be describedby a displacement vector u[x], such that T [x]=x+u[x]. The demons methodmay update the displacement field u iteratively. The displacement fieldmay be updated in each step by adding an increment in the direction ofthe image force field to the displacement field from the previous step.

In some embodiments, the Demons method may be utilized as the model forthe group of spatial transformation. Specifically, the movement of thevoxel along the X direction, the Y direction, and the Z direction,respectively, may be determined by the gradient of the gray level of thereference image:

$\begin{matrix}{u = \frac{2 \times \left( {m - f} \right)\left( {{\nabla m} + {\Delta \; f}} \right)}{\left( {m - f} \right)^{2} + {{{\nabla m} + {\Delta \; f}}}^{2}}} & (13)\end{matrix}$

Here for a voxel p, f=f(p) is the gray level at p in the referenceimage, m=m(p) is the gray level at p in the auxiliary image. ∇m and ∇fare the gradient of the functions m(p) and f(p), respectively.

In step 402, a similarity measure may be determined. In someembodiments, the similarity measure may be determined based upon themodel of spatial transformation. For example, for the spatialtransformation given by the affine transformation, the similaritymeasure may be the mutual information between the images including thereference image and the floating image. In some embodiments, thesimilarity measure may be normalized mutual information between theimages. In some embodiments, the similarity measure may be hybridnormalized mutual information between the images. In some embodiments,the similarity measure may be cross correlation, Mean squared Error,gradient cross-correlation, the difference in the gradient and sum ofsquared intensity differences between the two images, or the like, or acombination thereof.

In step 403, an optimization method may be determined. The determinationof the optimization method may depend upon the spatial transformationand/or the similarity measure. The optimization method considered heremay include a Powell method, a downhill simplex method, a gradientdescent method, a deepest gradient descending method, a conjugategradient method, a pseudo-Newton method, a quasi-Newton method, aleast-squares and Gauss-Newton method, aBroyden-Fletcher-Goldfarb-Shanno (BFGS) method, a limited-memoryBroyden-Fletcher-Goldfarb-Shanno (L-BFGS) method, a simulated annealingmethod, an ant colony optimization (ACO) method, a genetics algorithm, aLevenberg-Marquardt optimization method, a geometric hashing method, aparticle swarm optimization (PSO) method, a firefly algorithm (FA)method, or the like, or a combination thereof.

For example, the L-BFGs method may be implemented as an optimizationmethod. The limited memory BFGS (L-BFGS) method may be substantiallysimilar in its implementation to the BFGS method. The difference may liein the matrix update method. The BFGS corrections may be storedseparately, and when the available storage is used up, the oldestcorrection may be deleted to make space for the new one. All subsequentiterations may be of this form, i.e. one correction is deleted and a newone inserted.

For another example, the downhill simplex method may be implemented asan optimization method. The downhill simplex method may need onlyfunction evaluations, and not calculation of derivatives. In theN-dimensional space, a simplex is a polyhedron with N+1 vertices. N+1point may be chosen and an initial simplex may be defined in theimplementation of the downhill simplex method. The downhill simplexmethod may update the worst point iteratively by operations ofreflection, expansion, one-dimensional contraction, and multiplecontractions. For purposes of illustration, reflection may be involvedmoving the worst point (vertices) of the simplex (where the value of theobjective function is the highest) to a point reflected through theremaining N points. If this point is better than the previously bestpoint, then the method may attempt to expand the simplex, and theoperation may be called expansion. On the other hand, if the new pointis not better than the previous point, then the simplex may becontracted along one dimension from the highest point, and the operationmay be called contraction. Moreover, if the new point is worse than theprevious points, the simplex may be contracted along all dimensionstoward the best point and steps down the valley. By repeating thisseries of operations, the method may find the optimal solution.

It should be noted that the above description of the image processingmodule is provided for the purposes of illustration, not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the protecting scope of the presentdisclosure.

FIG. 5 is an exemplary flowchart illustrating a process for imageprocessing according to some embodiments of the present disclosure. Twoimages of an object may be input for a series of image registration tobe performed. In step 501, a first registration may be performed on thetwo images. The first registration may be a coarse registration. Thefirst registration may be based on spatial transformation. One may bereferred to as a reference image, and the other one may be referred toas a floating image. The first registration may designate a specificgroup of spatial transformations to be performed on the floating image.In some embodiments, the group of spatial transformation may be a groupof translations. The group of translation may correspond to the case ofEquation (3), where the coefficients a₁₂, a₁₃, a₂₁, a₂₃, a₃₁ and a₃₂ maybe set to zero, a₁₁, a₂₂ and a₃₃ may be set to 1. In some embodiments,the group of spatial transformations may be a group of rigid motions.The group of rigid motion may correspond to the case of Equation (3),where the matrix

$\begin{matrix}{{A = \begin{bmatrix}a_{11} & a_{12} & a_{13} \\a_{21} & a_{22} & a_{23} \\a_{31} & a_{32} & a_{33}\end{bmatrix}},} & (14)\end{matrix}$

may be an orthogonal matrix, i.e., AA^(t)=I, where

$\begin{matrix}{{I = \left\lbrack \begin{bmatrix}1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix} \right\rbrack},} & (15)\end{matrix}$

may be the identity matrix, and A^(t) may be the transpose of the matrixA. In some embodiments, the group of spatial transformation may be agroup of affine transformations.

The selection of the specific spatial transformation to be applied onthe floating image may depend the selection of the similarity measure,or referred to as the cost function. To measure thesimilarity/difference between the images, the cost function may be setby various ways. In some embodiments, the cost function may be given asmutual information. Based on the information theory, mutual informationmay represent the amount of information that one image may contain abouta second image. Mutual information may be maximized by aligning the twoimages in an optimal way. In some embodiments, the cost function may begiven as the mean squared error. In some embodiments, the cost functionmay be given as the relative entropy. In some embodiments, the costfunction may be given as the gradient relative entropy.

An optimization method may be utilized to find a desired spatialtransformation for the given cost function. As an example, the group ofspatial transformations may be include a group of affinetransformations. The cost function may be the mutual information or thenormalized mutual information. The optimization method may be thedownhill simplex method. As another example, the group of spatialtransformation may include a group of rigid motions. The cost functionmay be the relative entropy. The optimization method may be the Powellmethod. By performing the coarse registration on the floating image, anintermediate image may be obtained. One or more of the three images,i.e., the reference image, the floating image, and the intermediateimage may be provided to step 502.

In step 502, a second registration may be performed on the at least twoimages input from step 501. The second registration may be performed onthe basis of the first registration. The second registration may be afine registration. One image may be the reference image, and the othermay be the intermediate image. In some embodiments, the other image maybe the combination of intermediate image and the floating image. Thesecond registration may designate regions of interest (ROI) in thereference image. In some embodiments, the regions of interest may begiven as the feature points. A specific group of spatial transformationsdesignating the offsets of the feature points may be chosen. In someembodiments, free-form deformation (FFD) may be used. For example, the3^(rd) order B-spline model may be utilized as the model for the groupof spatial transformation. Specifically, the offsets of the voxel alongthe X direction, the Y direction, and the Z direction may be expressedas the B-spline function of offsets of the 4×4×4 control points adjacentto the voxel according to Equations (4)-(12).

After setting the model for the group of spatial transformations for thesecond registration, the similarity measure, or referred to as the costfunction, may be specified. To measure the similarity/difference betweenthe images, the cost function may be set by various ways. In someembodiments, the cost function may be given as mutual information. Basedon information theory, mutual information expresses the amount ofinformation that one image may contain about a second image. Mutualinformation may be maximized by aligning the two images in an optimalway. In some embodiments, the cost function may be given as the meansquared error. In some embodiments, the cost function may be given asthe relative entropy. In some embodiments, the cost function may begiven as the gradient relative entropy.

An optimization method may be utilized to find the desired spatialtransformation for the given cost function. In some embodiments, thegroup of spatial transformation may be given as the group of free-formdeformation based on the B-spline transformation. The cost function maybe the mutual information or the normalized mutual information. Theoptimization method may be the limited memoryBroyden-Fletcher-Goldfarb-Shannon (L-BFGS) method.

In some embodiments, the optimization method may be theBroyden-Fletcher-Goldfarb-Shannon (BFGS) method. By performing thesecond registration on the intermediate image, an auxiliary image may beobtained. One or all of the three images, i.e. the reference image,intermediate image, and the auxiliary image may be output to step 503.

In step 503, a third registration may be performed on the at least twoimages input from step 502. The third registration may be performed onthe basis of the first registration and/or the second registration. Thethird registration may be a super-fine registration. One image may bethe reference image, and the other may be the auxiliary image. In someembodiments, the other image may be the combination of auxiliary imageand the intermediate image or the floating image. The third registrationmay designate a feature point and/or a structure in the auxiliary image.A feature point may be a point within the image that is indicative ofthe characteristic of the image. For example, a feature point may be apoint with the highest gray level. For another example, a feature pointmay be a point to designate the positioning of rib(s). A structure maybe a formation of feature points. For example, the exhibition of rib(s)within the lung area may be a structure. For another example, the bloodvessel may be a structure. In some embodiment, a structure may be formedutilizing partly the feature points. A specific group of spatialtransformations designating the offsets of the feature points may bechosen. In some embodiments, a non-rigid registration model based on thegray level may be used. For example, the Demons method may be utilizedas the model for the group of spatial transformation. Specifically, themovement of the voxel along the X direction, the Y direction, and the Zdirection, respectively, may be determined by the gradient of the graylevel of the reference image, which have been descripted in Equation(15).

After setting the model for the group of spatial transformations for thethird registration, the similarity measure, or referred to as the costfunction, may be specified. To measure the similarity/difference betweenthe images, the cost function may be set by various ways. In someembodiments, the cost function may be given as mutual information. Insome embodiments, the cost function may be given as the mean squarederror. In some embodiments, the cost function may be given as therelative entropy. In some embodiments, the cost function may be given asthe gradient relative entropy.

An optimization method may be utilized to find the desired spatialtransformation for the given cost function. In some embodiments, thegroup of spatial transformations may be given as the group of free-formdeformations based on the non-rigid transformation. The cost functionmay be the mutual information or the normalized mutual information usedto measure the correlevance between the images. The optimization methodmay be the limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS)method. The limited memory BFGS method is substantially similar in itsimplementation to the BFGS method. The difference may lie in the matrixupdate method. The BFGS corrections may be stored separately, and whenthe available storage is used up, the oldest correction may be deletedto make space for the new one. All subsequent iterations may be of thisform, i.e. one correction is deleted and a new one inserted.Alternatively, the optimization method may be theBroyden-Fletcher-Goldfarb-Shannon (BFGS) method.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conduct under the teachingof the present disclosure. However, those variations and modificationsmay not depart from the protecting of the present disclosure. Forexample, step 501, step 502 and step 503 may be performed sequentiallyat an order other than that described above in FIG. 5. Step 501, step502 and step 503 may be performed concurrently or selectively. Step 501,step 502 and step 503 may be merged into a single step or divided into anumber of steps. In addition, one or more other operations may beperformed before/after or in performing step 501, step 502 and step 503.

FIG. 6 is an exemplary block diagram of the registration unit 330according to some embodiments of the present disclosure. Theregistration unit 330 may include a selection block 610, a computingblock 620, and a judgment block 630.

The selection block 610 may determine whether to perform a series ofregistrations based on the judgment block 630, and which registrationmay be performed. The selection block 610 may determine whichregistration model, similarity measure, optimization method to beperformed based on information transmitted from the judgment block 630.

The computing block 620 may perform a series of registrations determinedby the selection block 610 and/or judgment block 630. For example, thecomputing block 620 may compute the parameters of similarity measure andthe optimization method, such as gray level of voxels, indices of thecontrol points, the coordinate of a transformed voxel, image entropies.

The judgment block 630 may judge the image whether to perform a seriesof registrations based on the reference image, the desired floatingimage and the specification of the image processing system. The judgmentblock 630 may judge whether to compute the parameters of similaritymeasure, or the optimization method.

It should be noted that the above description of the image processingmodule is provided for the purposes of illustration, not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the protecting scope of the presentdisclosure. For example, the computing block 620 and the judgment block630 may be incorporated into one block.

FIG. 7 is an exemplary flowchart illustrating a process for performing aregistration according to some embodiments of the present disclosure.The registration may be a coarse registration. At least two images maybe obtained in step 701, one image may be the reference image, and theother image may be the floating image. In step 702, one or more featurepoints located on the reference image may be extracted, also called thesampling points. In step 703, according to the downhill simplex method,the solution space of the initial solution may be set. In an exemplarycase in which a 3-dimensional affine transformation is used as thespatial transformation, the number of parameters to be determined may beno less than 13. In step 704, the mutual information for each solutionin the solution space may be calculated according to the sampling pointsand Equation (4) involving the affine transformation. In step 705, thesolution space may be updated according to the updating rule of thedownhill simplex method and the mutual information of each solution inthe solution space. In step 706, the convergence criteria of thedownhill simplex method may be checked. If the convergence criteria issatisfied, then the solution obtained may be output as the optimalsolution, indicating the optimal affine transformation, into step 707.If the convergence criteria is not satisfied, then the algorithm may goback to step 704. In step 708, the intermediate image may be obtained byperforming the optimal affine transformation on the floating image.

It should be noted that the flowchart of performing the algorithm of thecoarse registration described above is provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, variousvariations and modifications may be conduct under the teaching of thepresent disclosure. However, those variations and modifications may notdepart from the protecting of the present disclosure.

FIG. 9 is an exemplary flowchart illustrating a process for performing aregistration according to some embodiments of the present disclosure.The registration may be a fine registration. The registration may beperformed on the basis of the registration described elsewhere in thepresent disclosure, for example, the registration as illustrated in FIG.7. At least two images may be obtained in step 901, one image may be thereference image, and the other image may be the floating image. Forexample, the floating image may be the intermediate image obtained fromthe coarse registration. In step 902, the feature points located on thereference image may be extracted, also called the sampling points. Instep 903, according to the L-BFGS method and the B-spline transformationmodel, the initial solution may be set. In step 904, the mutualinformation for each solution in the solution space would be calculatedaccording to the sampling points and the model of B-splinetransformation, together with the gradient of the mutual informationwith respect to the optimization variables. In step 905, the solutionspace would be updated according to the updating rule of the L-BFGSmethod and/or the mutual information of each solution in the solutionspace. In step 906, the convergence criteria of the L-BFGS method wouldbe checked. If the convergence criteria is satisfied, then the solutionobtained would be output as the optimal solution, indicating the optimalB-spline transformation, into step 907. If the convergence criteria isnot satisfied, then the algorithm would go to step 904. In step 908, theauxiliary image may be obtained by performing the optimal B-splinetransformation on the floating image.

It should be noted that the flowchart of performing the algorithm of theregistration described above in connection with FIG. 9 is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conduct under the teachingof the present disclosure. However, those variations and modificationsmay not depart from the protecting of the present disclosure.

FIG. 11 is an exemplary flowchart illustrating a process for performinga registration according to some embodiments of the present disclosure.The registration may be a super-fine registration. The registration maybe performed on the basis of the registration described elsewhere in thepresent disclosure, for example, the registration as illustrated in FIG.7 and/or the registration as illustrated in FIG. 9. At least two imagesmay be obtained in step 1101, one image may be the reference image, andthe other image may be the floating image. For example, the floatingimage may be the auxiliary image obtained from the fine registration. Instep 1102, the feature points located on the reference image may beextracted, also called the sampling points. The feature points may bechosen to be related to the gray level of the reference image. Forexample, the feature points may be chosen to be the locations ofnumerous minor blood vessels in the lung area of a CT image. In step1103, according to the L-BFGS method and the Demons transformationmodel, the initial solution may be set. In step 1104, the mutualinformation for each solution in the solution space would be calculatedaccording to the sampling points and the model of Demons transformation.In step 1105, the gradient of the mutual information with respect to theoptimization variables would be calculated. In step 1106, the solutionspace would be updated according to the updating rule of the L-BFGSmethod and/or the mutual information of each solution in the solutionspace. In step 1107, the convergence criteria of the L-BFGS method wouldbe checked. If the convergence criteria is satisfied, then the solutionobtained would be output as the optimal solution, singling out theoptimal Demons transformation, into step 1108. If the convergencecriteria is not satisfied, then the algorithm would go to step 1104. Instep 1108, the target image may be obtained by performing the optimalDemons transformation on the floating image.

It should be noted that the flowchart of performing the algorithm of thesuper-fine registration described above is provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, variousvariations and modifications may be conduct under the teaching of thepresent disclosure. However, those variations and modifications may notdepart from the protecting of the present disclosure.

FIG. 13 is an exemplary flowchart illustrating a process for performinga series of registration according to some embodiments of the presentdisclosure. At least two images may be obtained in step 1301, one imagemay be the reference image, and the other image may be the floatingimage. The two images may be two medical images of an object at variousstages. For example, the two images may be two CT images of a patient atdifferent times. For another example, the two images may be two DRimages of a patient at two stages. In step 1302, the two images may bepre-processed by performing, for example, image normalization, imagesegmentation, image recognition, image reconstruction, image smoothing,suppressing, weakening and/or removing a detail, a mutation, a noise, orthe like, or any combination thereof. In step 1303, a first registrationof two images may be implemented. For example, a coarse registration maybe implemented on the two images to generate an intermediate image. Thethree images, i.e. the reference image, the floating image, and theintermediate image may be output into step 1304. In step 1304, a secondregistration may be implemented on the three images. For example, a fineregistration may be implemented on the reference image and theintermediate image to produce an auxiliary image as the output image. Instep 1305, a third registration on the reference image and the auxiliaryimage may be implemented to produce the target image. In step 1306, atemporal subtraction image may be obtained by subtracting the targetimage from the reference image, illustrating the possible locations ofnew lesion, and/or the possible locations of diminished lesion. In step1307, an auto-detection may be implemented on the temporal subtractionimage to single out the region of interests for further processing. Instep 1308, quantification of the data from the images generated duringthe whole procedure may be produced, such as the volume of the lesion,the density of the region of interest, the object information about thepatient, the instrument information and the instruction information. Insome embodiments, the volume corresponding to each pixel or voxel in thelesion area (or the region of interest), of a medical image may beobtained, either in an empirical or a statistical way. The total volumeof the lesion may then be obtained by multiplying the number of pixelsor voxels in the lesion area, with the specific volume corresponding toeach pixel or voxel. In some embodiments, the density of the lesion area(or the region of interest) in each pixel or voxel of a medical imagemay be obtained, either in an empirical or a statistical way. The massof the lesion area (or the region of interest) corresponding to eachpixel or voxel may then be calculated. The total mass of the lesion area(or the region of interest) may then be obtained by summing up the massin all pixels or voxels in the lesion area. In step 1309 the imagestogether with the relevant data may be output to a display device.

It should be noted that the flowchart of performing the algorithm of thesuper-fine registration described above is provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For example, the size and location of the lesions of oneobject may be obtained using the procedure as illustrated in FIG. 13 atdifferent stages, either in an automatic or a manual way. The differenceof the size of the lesion, and/or the rate of change of the size oflesion may be derived taking into account of time when the images of theobject were generated. In some embodiments, the reference image, thefloating image, and the subtraction image may be displayed on the samedisplay device in a row. In some embodiments, the reference image, thefloating image, and/or the subtraction image may be displayed on thesame display device in a column. For persons having ordinary skills inthe art, various variations and modifications may be conduct under theteaching of the present disclosure. However, those variations andmodifications may not depart from the protecting of the presentdisclosure.

EXAMPLES

The following examples are provided for illustration purposes, and notintended to limit the scope of the present disclosure.

Example 1

FIG. 8A through FIG. 8C demonstrate some exemplary figures after thecoarse registration as illustrated in FIG. 7. FIG. 8A is a referenceimage, and FIG. 8B is a floating image, whereas FIG. 8C gives thetemporal subtraction image between the reference image and anintermediate image. The intermediate image was obtained from theoperation of the coarse registration between the reference image and thefloating image.

Example 2

FIG. 10A through FIG. 10D demonstrate some exemplary figures after thefine registration as illustrated in FIG. 9. FIG. 10A is a referenceimage, FIG. 10B is a floating image, and FIG. 10C gives the temporalsubtraction image between the reference image and an intermediate image.The intermediate image was obtained from the operation of the coarseregistration between the reference image and the floating image. Thereare still many shadowy areas exhibiting blood vessels in the lung area.FIG. 10D gives a temporal subtraction image between the reference imageand an auxiliary image. The auxiliary image was obtained from theoperation of the fine registration between the reference image and theintermediate image.

Example 3

FIG. 12A through FIG. 12D demonstrate some exemplary figures after thesuper-fine registration as illustrated in FIG. 11. FIG. 12A is areference image, FIG. 12B is a floating image, and FIG. 12D gives atemporal subtraction image between the reference image and a targetimage (or referred to as an auxiliary image). The target image wasobtained from the operation of the super-fine registration on thefloating image. There are but a few prominent shadowy areas in the lungarea. FIG. 12C gives a fused image between the reference image and thetemporal subtraction image. Note the red dots in FIG. 12C that designatethe corresponding feature points in FIG. 12 D, which is indicative ofthe change of the lesion.

Example 4

FIGS. 14A-14F illustrate six CT images that were generated based onimage registration according to an algorithm for performing a series ofregistration as illustrated in FIG. 13. FIGS. 14A, 14B and FIGS. 14D,14E are two sets of reference images and floating images, respectively,wherein FIG. 14C and FIG. 14F are the temporal subtraction imagesgenerated based on the various image registration or a combinationthereof as described in the present disclosure.

Example 5

FIGS. 15A-15F illustrate six X-ray images that were generated based onimage registration according to an algorithm for performing a series ofregistration as illustrated in FIG. 13. FIGS. 15A, 15B and FIGS. 15D,15E are two sets of reference images and floating images, respectively,wherein FIG. 15C and FIG. 15F are the temporal subtraction imagesgenerated based on the various image registration or a combinationthereof as described in the present disclosure.

Example 6

Fifty-nine CT images and 232 DR images were processed utilizing theimage registration described herein. To evaluate the quality ofsubtraction images, objective ratings by five radiologists and fivephysicists were utilized independently. A four-point rating scale belowwas then generated based on the objective ratings:

1 Bad: Most ribs (or Vessels) are not well registered;

2 Acceptable: Most ribs (or Vessels) are well registered, with someminor mis-registration error;

3 Good: Most ribs (or Vessels) are almost completely registered withsome very minor mis-registrations; and

4 Excellent: All ribs (or Vessels) are perfectly registered.

The rating for each case was determined in one of the four categoriesabove based on the average (or the majority) of the ratings provided bymultiple observers. The test result on the total 291 data images may beclassified into four categories: “excellent,” “good,” “acceptable,” and“bad” as above. Two hundred twenty-nine results are excellent, 56results are good, 2 results are acceptable, and 4 results are bad.Approximately 97.9% results are excellent or good.

As will be also appreciated, the above described method embodiments maytake the form of computer or controller implemented processes andapparatuses for practicing those processes. The disclosure can also beembodied in the form of computer program code containing instructionsembodied in tangible media, such as floppy diskettes, CD-ROMs, harddrives, or any other computer-readable storage medium, wherein, when thecomputer program code is loaded into and executed by a computer orcontroller, the computer becomes an apparatus for practicing theinvention. The disclosure may also be embodied in the form of computerprogram code or signal, for example, whether stored in a storage medium,loaded into and/or executed by a computer or controller, or transmittedover some transmission medium, such as over electrical wiring orcabling, through fiber optics, or via electromagnetic radiation,wherein, when the computer program code is loaded into and executed by acomputer, the computer becomes an apparatus for practicing theinvention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

The various methods and techniques described above provide a number ofways to carry out the application. Of course, it is to be understoodthat not necessarily all objectives or advantages described can beachieved in accordance with any particular embodiment described herein.Thus, for example, those skilled in the art will recognize that themethods may be performed in a manner that achieves or optimizes oneadvantage or group of advantages as taught herein without necessarilyachieving other objectives or advantages as taught or suggested herein.A variety of alternatives are mentioned herein. It is to be understoodthat some preferred embodiments specifically include one, another, orseveral features, while others specifically exclude one, another, orseveral features, while still others mitigate a particular feature byinclusion of one, another, or several advantageous features.

Furthermore, the skilled artisan will recognize the applicability ofvarious features from different embodiments. Similarly, the variouselements, features and steps discussed above, as well as other knownequivalents for each such element, feature or step, may be employed invarious combinations by one of ordinary skill in this art to performmethods in accordance with the principles described herein. Among thevarious elements, features, and steps some will be specifically includedand others specifically excluded in diverse embodiments.

Although the application has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the embodiments of the application extend beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses and modifications and equivalents thereof

Preferred embodiments of this application are described herein.Variations on those preferred embodiments will become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Itis contemplated that skilled artisans may employ such variations asappropriate, and the application may be practiced otherwise thanspecifically described herein. Accordingly, many embodiments of thisapplication include all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the application unlessotherwise indicated herein or otherwise clearly contradicted by context.

All patents, patent applications, publications of patent applications,and other material, such as articles, books, specifications,publications, documents, things, and/or the like, referenced herein arehereby incorporated herein by this reference in their entirety for allpurposes, excepting any prosecution file history associated with same,any of same that is inconsistent with or in conflict with the presentdocument, or any of same that may have a limiting affect as to thebroadest scope of the claims now or later associated with the presentdocument. By way of example, should there be any inconsistency orconflict between the description, definition, and/or the use of a termassociated with any of the incorporated material and that associatedwith the present document, the description, definition, and/or the useof the term in the present document shall prevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method comprising: designating a first image ofan object as a reference image, the reference image comprising at leasta reference feature point and a reference structure; obtaining a secondimage of the object, the second image comprising a feature point and astructure, the feature point corresponding to the reference featurepoint of the reference image, the structure corresponding to thereference structure of the reference image; performing a firstregistration of the second image to obtain a first registered image, thefirst registration comprising an affine transformation, the firstregistered image comprising the feature point and the structure;performing a second registration of the first registered image to obtaina second registered image, the second registration comprising aligningthe structure in the first registered image with the reference structurein the reference image, the second registered image comprising thefeature point; performing a third registration of the second registeredimage to obtain a third registered image, the third registrationcomprising aligning the feature point in the second registered imagewith the reference feature point in the reference image; and subtractingthe third registered image from the reference image to obtain asubtraction image comprising the feature point or the structure.
 2. Themethod of claim 1, wherein the second registration is based on the freeform deformation model transformation.
 3. The method of claim 1, whereinthe third registration is based on the Demons model transformation. 4.The method of claim 1, wherein the first registration is based on anoptimization of either mutual information or a mean squared error. 5.The method of claim 4, wherein the first registration comprises adownhill simplex method.
 6. The method of claim 1, wherein the secondregistration is based on an optimization of either mutual information ora mean squared error.
 7. The method of claim 6, wherein the secondregistration comprises an L-BFGS method.
 8. The method of claim 1,wherein the third registration is based on an optimization of eithermutual information or a mean squared error.
 9. The method of claim 8,wherein the third registration comprises an L-BFGS method.
 10. Themethod of claim 1, wherein the first image and the second image aretaken at different times.
 11. The method of claim 1, wherein the firstimage, the second image, and the subtraction image are displayed on asame display device.
 12. The method of claim 1, wherein the referencefeature point in the first image and the feature point in the secondimage are displayed in the subtraction image.
 13. The method of claim 1,wherein the feature point and the structure in the subtraction image arefused with the reference image for showing the change of the featurepoint or the structure in the subtraction image over the referencefeature point or the reference structure.
 14. The method of claim 1further comprising: identifying, in the subtraction image, a region ofinterest including the feature point in the second image, andquantifying a pathological change of the feature point in the region ofinterest.
 15. A non-transitory computer-readable medium containinginstructions that, when executed by a processor, cause the processor toperform operations comprising: designating a first image of an object asa reference image, the reference image comprising at least a referencefeature point and a reference structure; obtaining a second image of theobject, the second image comprising a feature point and a structure, thefeature point corresponding to the reference feature point of thereference image, the structure corresponding to the reference structureof the reference image; performing a first registration of the secondimage to obtain a first registered image, the first registrationcomprising an affine transformation, the first registered imagecomprising the feature point and the structure; performing a secondregistration of the first registered image to obtain a second registeredimage, the second registration comprising aligning the structure in thefirst registered image with the reference structure in the referenceimage, the second registered image comprising the feature point;performing a third registration of the second registered image to obtaina third registered image, the third registration comprising aligning thefeature point in the second registered image with the reference featurepoint in the reference image; and subtracting the third registered imagefrom the reference image to obtain a subtraction image.
 16. A system ofimage processing, comprising: an acquisition module configured toacquire a first image and a second image; and an image processing moduleconfigured to: designate the first image as a reference image; designatethe second image as a floating image; perform image registration basedon the reference image and the floating image, and perform subtractionbased on the image registration and the reference image to acquire asubtraction image.
 17. The system of claim 16, wherein the imageprocessing module comprises a post-processing unit to perform lesiondetection and lesion measurement.
 18. The system of claim 16, whereinthe image processing module comprises a control unit configured tocontrol the performance of a series of registrations.
 19. The system ofclaim 16, wherein the image processing module comprises an image displayunit for displaying the reference image, the floating image, and thesubtraction image.